try:
    from sklearn.datasets import fetch_openml
    mnist = fetch_openml('mnist_784', version=1, cache=True)
    mnist.target = mnist.target.astype(np.int8) # fetch_openml() returns targets as strings
    sort_by_target(mnist) # fetch_openml() returns an unsorted dataset
except ImportError:
    from sklearn.datasets import fetch_mldata
    mnist = fetch_mldata('MNIST original')
mnist["data"], mnist["target"]

---------------------------------------------------------------------------
MemoryError                               Traceback (most recent call last)
<ipython-input-2-3f28d0b20c3d> in <module>()
      1 try:
      2     from sklearn.datasets import fetch_openml
----> 3     mnist = fetch_openml('mnist_784', version=1, cache=True)
      4     mnist.target = mnist.target.astype(np.int8) # fetch_openml() returns targets as strings
      5     sort_by_target(mnist) # fetch_openml() returns an unsorted dataset

C:\Users\taketoshi\Anaconda3\lib\site-packages\sklearn\datasets\openml.py in fetch_openml(name, version, data_id, data_home, target_column, cache, return_X_y)
    641                           k in data_columns + target_column}
    642 
--> 643     X, y = _convert_arff_data(arff['data'], col_slice_x, col_slice_y, shape)
    644 
    645     is_classification = {col_name in nominal_attributes

C:\Users\taketoshi\Anaconda3\lib\site-packages\sklearn\datasets\openml.py in _convert_arff_data(arff_data, col_slice_x, col_slice_y, shape)
    246                            dtype='float64', count=count)
    247         data = data.reshape(*shape)
--> 248         X = data[:, col_slice_x]
    249         y = data[:, col_slice_y]
    250         return X, y

MemoryError: